A Robust Face Recognition Algorithm for Real-World Applications

We developed a local appearance-based face recognition algorithm using discrete cosine transform, which is a generic, robust, and fast face recognition algorithm that has been deployed for several real-world person identification applications. The proposed face recognition approach divides the input face image into local blocks and processes each local block using discrete cosine transform. The local representation provides robustness against appearance variations in local regions caused by factors such as facial occlusion or expression, whereas utilizing frequency information provides robustness against changes in illumination. The algorithm has been extensively tested both using standard benchmark databases —AR, CMU PIE, FRGC, Yale B, Extended Yale B— and using the data collected from real-world applications —person identification in smart rooms, entrance monitoring, visitor interface, person re-identification in TV series—. The experimental results show that, the algorithm can successfully handle facial appearance variations caused by uncontrolled recording conditions, expression, occlusion, and illumination. Moreover, the systems based on this algorithm have been found to work reliably under real-world conditions.

3D Face Recognition

In the system, 3D point clouds are registered to provide dense correspondence between faces. Depth images are constructed from the corresponding well-registered point clouds. The system utilizes depth map images to extract local features and performs identification using local appearance-based face recognition.

Open-set Face Recognition

In the system, faces are automatically detected and registered. The local appearance-based face recognition method is utilized for representing the faces. An identity verification component is trained for every known subject in the database. Open-set identification is performed via a series of verification processes.

The system has been developed as a visitor interface, where a visitor looks at the monitor before knocking on the door. A welcome message is displayed on the screen. While the visitor is looking at the welcome message, the system identifies the visitor unobtrusively without needing person’s cooperation. According to the identity of the person, the system customizes the information that it conveys about the host.